State Estimation for Large-Scale Power System Based on Hybrid CPU-GPU Platform

被引：0
|
作者：
Xia, Yue [1 ]
Chen, Ying [1 ]
Ren, Zhengwei [1 ]
Huang, Shaowei [1 ]
Wang, Mingxuan [1 ]
Lin, Meng [1 ]
机构：
[1] Tsinghua Univ, Dept Elect Engn, Beijing, Peoples R China
来源：
2017 IEEE CONFERENCE ON ENERGY INTERNET AND ENERGY SYSTEM INTEGRATION (EI2) | 2017年
基金：
中国国家自然科学基金;
关键词：
State estimation; GPU; hybrid; large scale acceleration;
D O I：
暂无
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
The acceleration of state estimation has a significant beneficial effect on monitoring, dispatching and stability of power system. The increasing scale of power system brings more and more pressure to the state estimation of power system. The graphic processing unit (GPU) which features the massive concurrent threads and excellent floating point performance brings a new chance to the area of state estimation of power system. In this paper, a state estimation method which bridges merits of GPU and central processing unit (CPU) is introduced. Case studies demonstrate that the proposed state estimation method offers a significant improvement in terms of efficiency over other existing method.
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收藏
页数：6
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